---
title: Agno
description: "Track your Agno agents, teams, and workflows with AgentOps"
---

## Video Tutorial

<iframe
  width="560"
  height="315"
  src="https://www.youtube.com/embed/M4e1Ybkn_K0"
  title="AgentOps + Agno Integration Tutorial"
  frameborder="0"
  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
  allowfullscreen
></iframe>

[Agno](https://docs.agno.com) is a modern AI agent framework for building intelligent agents, teams, and workflows. AgentOps provides automatic instrumentation to track all Agno operations including agent interactions, team coordination, tool usage, and workflow execution.

## Installation

Install AgentOps and Agno:

<CodeGroup>
  ```bash pip
  pip install agentops agno 
  ```
  ```bash poetry
  poetry add agentops agno 
  ```
  ```bash uv
  uv pip install agentops agno 
  ```
</CodeGroup>

## Setting Up API Keys

You'll need API keys for AgentOps and your chosen LLM provider:
- **AGENTOPS_API_KEY**: From your [AgentOps Dashboard](https://app.agentops.ai/)
- **OPENAI_API_KEY**: From the [OpenAI Platform](https://platform.openai.com/api-keys) (if using OpenAI)
- **ANTHROPIC_API_KEY**: From [Anthropic Console](https://console.anthropic.com/) (if using Claude)

Set these as environment variables or in a `.env` file.

<CodeGroup>
  ```bash Export to CLI
  export AGENTOPS_API_KEY="your_agentops_api_key_here"
  export OPENAI_API_KEY="your_openai_api_key_here"
  export ANTHROPIC_API_KEY="your_anthropic_api_key_here"  # Optional
  ```
  ```txt Set in .env file
  AGENTOPS_API_KEY="your_agentops_api_key_here"
  OPENAI_API_KEY="your_openai_api_key_here"
  ANTHROPIC_API_KEY="your_anthropic_api_key_here"  # Optional
  ```
</CodeGroup>

## Quick Start

```python
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()


from agno.agent import Agent
from agno.team import Team
from agno.models.openai import OpenAIChat

# Initialize AgentOps
import agentops
agentops.init(api_key=os.getenv("AGENTOPS_API_KEY"))

# Create and run an agent
agent = Agent(
    name="Assistant",
    role="Helpful AI assistant",
    model=OpenAIChat(id="gpt-4o-mini")
)

response = agent.run("What are the key benefits of AI agents?")
print(response.content)
```

## AgentOps Integration

### Basic Agent Tracking

AgentOps automatically instruments Agno agents and teams:

```python
import agentops
from agno.agent import Agent
from agno.team import Team
from agno.models.openai import OpenAIChat

# Initialize AgentOps - this enables automatic tracking
agentops.init(api_key=os.getenv("AGENTOPS_API_KEY"))

# Create agents - automatically tracked by AgentOps
agent = Agent(
    name="Assistant",
    role="Helpful AI assistant",
    model=OpenAIChat(id="gpt-4o-mini")
)

# Create teams - coordination automatically tracked
team = Team(
    name="Research Team", 
    mode="coordinate", 
    members=[agent]
)

# All operations are automatically logged to AgentOps
response = team.run("Analyze the current AI market trends")
print(response.content)
```

## What Gets Tracked

AgentOps automatically captures:

- **Agent Interactions**: All agent inputs, outputs, and configurations
- **Team Coordination**: Multi-agent collaboration patterns and results  
- **Tool Executions**: Function calls, parameters, and return values
- **Workflow Steps**: Session states, caching, and performance metrics
- **Token Usage**: Costs and resource consumption across all operations
- **Timing Metrics**: Response times and concurrent operation performance
- **Error Tracking**: Failures and debugging information


## Dashboard and Monitoring

Once your Agno agents are running with AgentOps, you can monitor them in the [AgentOps Dashboard](https://app.agentops.ai/):

- **Real-time Monitoring**: Live agent status and performance
- **Execution Traces**: Detailed logs of agent interactions
- **Performance Analytics**: Token usage, costs, and timing metrics
- **Team Collaboration**: Visual representation of multi-agent workflows
- **Error Tracking**: Comprehensive error logs and debugging information

## Examples

<CardGroup cols={2}>
  <Card title="Basic Agents and Teams" icon="users" href="/v2/examples/agno">
    Learn the fundamentals of creating AI agents and organizing them into collaborative teams
  </Card>
  
  <Card title="Async Operations" icon="bolt" href="https://github.com/AgentOps-AI/agentops/blob/main/examples/agno/agno_async_operations.ipynb">
    Execute multiple AI tasks concurrently for improved performance using asyncio
  </Card>
  
  <Card title="Research Team Collaboration" icon="magnifying-glass" href="https://github.com/AgentOps-AI/agentops/blob/main/examples/agno/agno_research_team.ipynb">
    Build sophisticated multi-agent teams with specialized tools for comprehensive research
  </Card>
  
  <Card title="RAG Tool Integration" icon="database" href="https://github.com/AgentOps-AI/agentops/blob/main/examples/agno/agno_tool_integrations.ipynb">
    Implement Retrieval-Augmented Generation with vector databases and knowledge bases
  </Card>
  
  <Card title="Workflow Setup with Caching" icon="diagram-project" href="https://github.com/AgentOps-AI/agentops/blob/main/examples/agno/agno_workflow_setup.ipynb">
    Create custom workflows with intelligent caching for optimized agent performance
  </Card>
</CardGroup>

